The use of digital data compression techniques to minimize transmission time has received considerable attention in recent years due to rapidly increasing communications of digital data and the concomitant increase in the importance of line charges. Obviously, if a given amount of intelligence can be transmitted in less time, there will be a commensurate reduction in line costs.
There are various known techniques for achieving the compression of data. One technique is analog curve fitting wherein a signal sequence is represented by a curve or waveform and the coefficients of a polynomial expression fitted to curve are transmitted. At the receiving location, the curve is reconstructed from those coefficients. However, this compression technique is subject to error resulting from the inability, in general, to provide an exact curve fit to a waveform. It is, therefore, unsuitable for transmission of a digital data stream where absolute accuracy is required. For example, transmission of text or financial data must usually be free from error.
In another technique, known as Huffman coding, blocks of characters are encoded according to their frequencies of occurrence. In long messages, this technique can provide highly efficient codes. However, with short to medium length messages, the codes must be changed from time to time to reflect changes in frequencies of occurrence. Whenever such a change is made, coding instructions must be transmitted to the receiving location to enable the receiver to decode the transmitted messages. Obviously, the requirement to transmit these instructions reduces the savings in transmission time gained by the coding of the message information.